ABSTRACT

Tropical Mountain Forest (TMF) provides important ecological functions like evapotranspiration (ET) that supplies moisture and energy to the atmosphere. ET observations are scarce and difficult to accomplish particularly in areas of high heterogeneity where TMF are. Remote sensing (RS) allows to quantify and to determine ET spatial variation at the landscape level. Detail imaginary improves high spatial variability retrieval. Thought the greater detail introduces cast shadows by trees which hamper image interpretation. The objective of this study is to characterize ET estimation for the TMF of the southern Ecuadorian Andes by combining meteorological data with high-resolution satellite images. Shadows from high resolution images were masked out by applying focal statistics. The analysis included two meteorological periods typical of the area; a wet period when rain prevails and a dry period when precipitation is more sporadic. The reference evapotranspiration (ET₀) was calculated using the FAO-Penman Montheid method by applying data obtained from an automatic weather station. The enhanced vegetation index (EVI) was derived from 2 m resolution WorldView2 satellite images. Results showed a lower ET mean value during the wet period: 1.54 mm day⁻¹ compared to 2.37 mm day⁻¹. Two forest types, differentiated from its structural composition and topographical position (ravine and ridge), marked ET spatial variation. Ravine forest that has a more dense and closed canopy showed higher ET values for both meteorological conditions. A comparison between ET estimations and ET field measurements from a scintillometer device showed a good agreement (coefficient of correlation $r$ = 0.89) that proves the validity of the method. This study demonstrates that the application of high spatial resolution improves ET estimation in TMF especially when shadows are removed. Also, emphasizes the importance of analysing spatial heterogeneity to properly assess ecosystem water flux terms.

摘要

热带山地森林（TMF）提供重要的生态功能，例如蒸散（ET），向大气供应水分和能量。ET观测稀少且难以完成，特别是在TMF高度异质性较高的地区。遥感（RS）可以量化和确定景观级别的ET空间变化。虚构细节改善了高空间变异性的检索。认为更详细的细节会引入树木投射的阴影，这会影响图像的解释。这项研究的目的是通过结合气象数据和高分辨率卫星图像来表征厄瓜多尔南部安第斯山脉TMF的ET估计。通过应用焦点统计信息，可以掩盖高分辨率图像的阴影。分析包括该地区典型的两个气象时期。雨水充沛的潮湿时期和降水较少的干旱时期。参考蒸散量（ET₀）是使用FAO-Penman Montheid方法通过应用从自动气象站获得的数据来计算的。增强的植被指数（EVI）来自2 m分辨率的WorldView2卫星图像。结果显示，在湿润时期的ET平均值较低：1.54 mm天^-1相比于2.37 mm天^-1。根据其结构组成和地形位置（沟壑和山脊）的不同，两种森林类型具有明显的ET空间变化。在两种气象条件下，拥有更密密的冠层的沟壑森林显示出更高的ET值。闪烁仪设备的ET估计值与ET场测量值之间的比较显示出良好的一致性（相关系数$\mathrm{[R}$= 0.89），证明了该方法的有效性。这项研究表明，高空间分辨率的应用可以改善TMF中的ET估计，尤其是在去除阴影的情况下。另外，强调分析空间异质性以正确评估生态系统水通量项的重要性。